Jul 2026· Journal of Electronic Imaging (JEI)· Vol 35, pp. 043024 - 043024· 0 citations· 39 references
Engineering
TL;DR
RestoreGait is introduced, an end-to-end framework designed to actively recover occluded gait cues through a lightweight pseudo-3D spatiotemporal decoupled inpainting module that decouples spatial contour restoration from temporal motion aggregation, thereby effectively utilizing visible information across multiple frames.
Abstract
Abstract. Gait recognition represents a nonintrusive biometric modality that suffers from substantial performance degradation when subjected to real-world occlusions, such as those caused by bushes, pillars, or crowds. Current approaches either passively suppress occluded regions or employ image inpainting techniques that fail to account for temporal coherence and identity consistency. We introduce RestoreGait, an end-to-end framework designed to actively recover occluded gait cues through a lightweight pseudo-3D spatiotemporal decoupled inpainting module. This module decouples spatial contour restoration from temporal motion aggregation, thereby effectively utilizing visible information across multiple frames. Furthermore, an uncertainty-aware soft-gating mechanism is employed to suppress inpainting artifacts of low confidence, whereas a semantic identity consistency loss ensures that the restored sequences maintain discriminative identity features within the deep embedding space, rather than merely achieving visual realism. Extensive experiments on the SUSTech1K, OccGait, Gait3D, and GREW datasets demonstrate that the proposed method yields consistent improvements over strong baselines with negligible computational overhead, demonstrating strong and consistent performance on occluded gait benchmarks.
Gait recognition has emerged as an important biometric modality due to its non-invasive nature and suitability for surveillance and security applications. However, achieving robustness under real-world variations in viewpoint, clothing, and carrying conditions remains a significant challenge. This paper introduces \textbf{AttIncGait}, a deep learning framework that integrates Inception-based multi-scale feature extraction with dual-path attention for effective gait recognition. Unlike prior methods that treat attention as an auxiliary or late-stage refinement, our approach embeds spatial and channel attention directly within Inception modules, enabling simultaneous multi-scale representation and adaptive relevance weighting. This structural integration enhances discriminative capability while preserving computational efficiency. Experiments on CASIA-B and OU-MVLP datasets demonstrate state-of-the-art performance: 97.5% accuracy on OU-MVLP and a 2.6% improvement over the best existing method under clothing variation in CASIA-B. Ablation studies further reveal that spatial and channel attention individually improve accuracy, while their joint integration yields an overall +8.5% gain on OU-MVLP. These results validate the effectiveness of attention-driven multi-scale fusion for gait recognition and highlight the potential of AttIncGait for real-world biometric identification and mobility analysis.
S. Mandlik, R. Labade, Sachin Chaudhari et al.· ELCVIA Electronic Letters on...· 0 citations
This novel E-VFI framework diverges from approaches reliant on direct image-level supervision by constructing multilevel, degradation-insensitive semantic perceptual supervisory signals to enhance the perceptual realism and multi-scene generalization of the model's predictions.
Yuhan Liu, Linghui Fu, Zheng Yang et al.· Neural Information Processin...· 1 citation
High-Compression videos suffer from severe distortions, among which degradation in person regions has the greatest impact on viewers’ immersive experience. Existing quality enhancement techniques usually focus on overall image denoising or super-resolution, often overlooking the crucial recovery of fine structures in these essential person regions. To address these challenges, the research introduces a novel framework titled Person Region Restoration Driven by Perceptual Fidelity (PRRDPF), which combines long-range dependency features with perceptual structure loss for enhanced generative restoration. Specifically, first, the research constructs a high-fidelity distorted person-region dataset via a closed-loop degradation pipeline, addressing the lack of paired datasets. Secondly, a Temporal Gated Fusion (TGF) block is designed to use gated convolutions for selectively recovering high-frequency features while capturing local and global dependencies. Finally, a Structural Similarity Index Measure (SSIM)-based dynamic weighted adversarial loss is proposed to prioritize the restoration of visual texture details. Experimental results validate that PRRDPF significantly outperforms the best models in Peak Signal-to-Noise Ratio (PSNR), SSIM, and Learned Perceptual Image Patch Similarity (LPIPS), effectively mitigating artifacts and enhancing clarity in person visuals. This framework presents a promising approach for intelligent video coding integrated with generative artificial intelligence and holds significant potential for practical applications.
Linyun Liu, Li Yu, Jiaxin Zeng et al.· IEEE Signal Processing Lette...· 0 citations
The confidence-guided hybrid network (CGHNet) is proposed, a parallel three-branch framework that jointly performs frequency-decoupled local restoration, global context modeling, and pixel-wise degradation prior estimation and its key component is a confidence-guided feature purification mechanism.
Xiaohui Kou, Yang Yan, Qiuyan Wang et al.· Journal of Supercomputing· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
